Design and application of real-time network abnormal traffic detection system based on Spark Streaming
by FuCheng Pan; DeZhi Han; Yuping Hu
International Journal of Embedded Systems (IJES), Vol. 11, No. 5, 2019

Abstract: In order to realise the rapid analysis and identification of abnormal traffic in real-time networks, a distributed real-time network abnormal traffic detection system (DRNATDS) was designed, which could effectively analyse abnormal network traffic. DRNATDS provided effective real-time big data analysis platform and guaranteed network security. The paper proposes K-means algorithm based on relative density and distance, integrated with Spark Streaming and Kafka. It could effectively detect various network attacks under real-time data stream. The experimental results show that DRNATDS has good high availability and stability. Compared to other algorithms, K-means algorithm based on relative density and distance could more effectively identify abnormal network traffic and improve the recognition rate.

Online publication date: Tue, 24-Sep-2019

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